With high integration and large capacity of a Large Scale Integration (LSI), a circuit dimension required for a semiconductor element has become increasingly narrowed. By using an original image pattern (that is, a mask or a reticle, hereinafter collectively referred to as a mask) in which a circuit pattern is formed, the pattern is exposed and transferred onto a wafer by a reduction projection exposure apparatus, called a stepper or a scanner, to form a circuit on the wafer, thereby producing a semiconductor element.
Since LSI production requires a large manufacturing cost, it is crucial to improve the production yield. On the other hand, in a contemporary semiconductor device, a pattern having a line width from ten nanometers to twenty nanometers is required to be formed. At this point, a defect of the mask pattern can be cited as a large factor of degradation in the production yield. As the dimensions of an LSI pattern to be formed on a semiconductor wafer becomes finer, the defect of the mask pattern becomes finer.
As fluctuations of various process conditions are absorbed by enhancing dimensional accuracy of the mask, it is necessary to detect the defect of the extremely small pattern in a mask inspection. Therefore, high accuracy is required for an inspection apparatus that inspects patterns of a mask.
In the mask inspection apparatus, light emitted from a light source is irradiated onto a mask through an optical system. The mask is loaded and chucked on a stage, and the illuminated light scans the mask by movement of the stage. The light transmitted through or reflected by the mask, images on a sensor through lenses of an optical system. Then, the defect inspection with respect to the mask is performed based on the optical images acquired by the sensor.
A die-to-die comparison inspection method and a die-to-database comparison inspection method are known as examples of mask inspection methods performed using the mask inspection apparatus. In the die-to-die comparison method, an optical image of a pattern and another optical image of the identical pattern at a different position are compared with each other. On the other hand, in the die-to-database comparison method, a reference image generated from design data used in mask production and an optical image of the actual pattern formed in the mask are compared with each other.
In both of the inspection methods, using a proper defect determination logic (algorithm), a reference image is compared with an image in which a defect determination should be performed. The defect determination is performed when a defect reaction value calculated using the defect determination logic (algorithm) exceeds a predetermined defect determination threshold value.
A plurality of defect determination logics (algorithms) are simultaneously used, for example, level comparison and derivative value comparison. In the level comparison, luminance values in pixels of identical pattern portions of the reference image (a first die image or the reference image) and the inspection target image (a second die image or the optical image of the actual mask pattern) are compared with each other. In the derivative value comparison, a derivative value in a pattern tangential direction of the reference image is compared with a derivative value in a pattern tangential direction of a corresponding portion of the inspection target image. In both of the level comparison and the derivative value comparison, the defect reaction value calculated using the defect determination logic (algorithm) is increased along with an increase of the influence of the defect.
For this reason, in each defect determination logic (algorithm), the defect reaction value equivalent to the permissible influence of the defect is defined as a defect determination threshold value, the defect determination is performed when the defect reaction value exceeds the defect determination threshold value, and detection sensitivity of the defect determination is fixed. The plurality of defect determination logics (algorithms) are concurrently applied, and the defect determination is performed when one of the defect determination logics (algorithms) exceeds the defect determination threshold value.
The defect determination threshold value is a criterion that is used to determine the defect when the defect reaction value exceeds the defect determination threshold value. Therefore, when a numerical value of the defect determination threshold value is increased, the defect determination threshold value acts to permit a large error caused by the defect. On the other hand, when the numerical value of the defect determination threshold value is decreased, the defect determination threshold value acts to permit a small error caused by the defect.
For the die-to-database comparison inspection method, it can be concluded that the reference image generated from the design pattern data is always correct, and it can be concluded that the defect exists in the optical image of the actual mask pattern when the defect reaction value of the defect determination logic (algorithm) exceeds the defect determination threshold value. For the die-to-die comparison inspection method, when the defect is included in one of the first die image and the second die image, the defect reaction value of the defect determination logic (algorithm) exceeds the defect determination threshold value, and thereby the defect determination is performed. In this case, after the inspection, an operator performs a review to confirm the defect, and the defective die is identified.
In order to acquire an optical image, a charge accumulation type time delay integration (TDI) sensor and a sensor amplifier that amplifies the output of the TDI sensor are used. In a case of a half-tone type phase shift mask inspection using a transmitted light, a defect is determined by recognizing a mask pattern by a light signal intensity of the acquired sensor image through the detection optical system like a chrome mask, because the sensor image has enough contrast between the light shielding film and the glass substrate of the halftone type phase shift mask.
Depending on the shape of the defect, the reflection image may have a favorable contrast, so there is also an inspection method using a reflection inspection optical system for the purpose of a particle inspection function or the like. In addition, there is adopted a method of performing defect inspection with high detection sensitivity by correcting out-of-focus of transmitted irradiation light by a variation in a thickness of a mask.
The defect of the mask is determined based on whether the line width or the amount of misplacement other than the shape defect falls within a predetermined error range. Specifically, irregularities (edge roughness) of a pattern edge, a line width abnormality of the pattern, and an abnormality of a gap between patterns adjacent to each other due to the misplacement can be cited as an example. The amount of misplacement is determined by comparing the reference image generated from a database as a reference to a X-direction error and a Y-direction error of an edge position of the optical image using the proper defect determination logic (algorithm). There is also disclosed a method for producing a misplacement map (for example, see JP 2013-064632 A). In the defect determination logic (algorithm) suitable for the calculation of the X-direction error and the Y-direction error, the defect reaction value is calculated according to an amount of size error, the defect reaction value equivalent to the permissible size error is defined as the defect determination threshold value, and the determination of a misplacement defect is made when the defect reaction value exceeds the defect determination threshold value. A variation within the size error of a permissible degree of misplacement is recorded in the misplacement map.
In the embodiment, the defect shape of the mask and the convex or concave pattern are described based on the transmitted light image. That is, the term “the pattern is convex” means that a white portion is seen as convex in the transmitted light image, and the term “the pattern is concave” means that the white portion is seen as concave in the transmission image.
As described above, the amount of misplacement is obtained by measuring the X-direction error and the Y-direction error of the edge position of the optical image with respect to the reference image generated from the database as a reference. However, when the convex defect or the concave defect is generated in a specific direction, the measured misplacement amount has a negative influence on the alignment between layers in a multi-layer construction, and a potential margin is widened.
In the mask inspection, the defect is detected by comparing the reference image and the optical image in a rectangular region (hereinafter, referred to as a frame) having a size of several tens of micrometers in the mask. An amount of misplacement of the pattern is measured before alignment is performed on the misplacement of the pattern between the reference image and the optical image in the frame. Then a map of the amount of misplacement of the pattern in the mask surface, namely, the misplacement map is generated. Therefore, in the mask inspection, it is necessary to more accurately perform the defect determination by simultaneously using the misplacement map.
The present invention has been devised to solve the problem described above. An object of the present invention is to provide a mask inspection apparatus and mask inspection method for being able to improve the inspection accuracy of the defect determination by simultaneously using the misplacement map in the mask defect inspection when the convex defect or the concave defect is generated in a direction in which the misplacement is generated on the observed pattern.